Comparison with state-of-the-art. Table REF  reports a comparison with existing approaches, namely Surface [1]} and NCNN variants [2]} on both splits, grouping unsupervised and non-learned methods on left (a) and supervised ones on right (b).
Since our framework can be trained on ground-truth labels as well, we report this additional experiment, marked with \(\dag \) , to compare it with supervised methods directly. While this slightly improves the performance on the CV split, which is labeled with the same semi-automatic procedure of the training set, it leads to worse results on the accurate ground-truth maps of the 142 split. This outcome is not surprising since several outliers do not have a corresponding ground-truth value on KITTI CV and are never observed during supervised training over it. In contrast, as reported in the table, our unsupervised strategy is intrinsically unaffected by this bias.
